Natalie Harvey Supervisors: Helen Dacre & Robin Hogan
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© University of Reading 2008 www.reading.ac.uk
Evaluation of Boundary-Layer Type in Weather Forecast Models Using Long-Term Doppler Lidar ObservationsNatalie HarveySupervisors: Helen Dacre & Robin Hogan
9/5/2012
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Questions
• How is the boundary layer modelled?• Observational diagnosis of boundary-
layer type?• How does the Met Office 4km model
boundary-layer type compare to the observed?
• What next?
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How is the boundary layer modelled?
Lock et al. (2000)
+ Type 7: unstable shear dominated
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Stability
Lock et al. (2000)
+ Type 7: unstable shear dominated
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Cloud type - stratocumulus
Lock et al. (2000)
+ Type 7: unstable shear dominated
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Cloud type - cumulus
Lock et al. (2000)
+ Type 7: unstable shear dominated
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Decoupled layer
Lock et al. (2000)
+ Type 7: unstable shear dominated
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2 layers of cloud
Lock et al. (2000)
+ Type 7: unstable shear dominated
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Model Boundary Layer Diagnosis
Type 2 Type 1 Type 5 Type 6 Type 4 Type 3
stable?
cumulus?
decoupled stratocumulu
s?
cumulus?
decoupled stratocumulu
s?
decoupled stratocumulu
s?
Y
Y Y Y
Y
N
N N N
NNY
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What about observations?
• Unstable?
•Cloud type?
•Decoupled cloud layer?
•2 cloud layers?
Sonic anemometer
Doppler lidar – w skewness and variance
Doppler lidar – w variance
Doppler lidar backscatter
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Example day – 18/10/2009
• Usually the most probable type has a probability greater than 0.9
Harvey, Hogan and Dacre (2012, in revision)
most probable boundary layer type
IV: decoupled
stratocumulus
IIIb: well mixed
stratocumulus topped
II: decoupled stratocumulus over a stable
layer
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Observational decision tree
stable, well mixed and
cloudy
stratocumulus over stable
unstable, well mixed & cloudy decoupled
stratocumulus
stratocumulus over cumulus
cumulus capped
stable, well mixed
unstable, well mixed
stable? stable?
stratocumulus?
stratocumulus &
decoupled?
decoupled?
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Most probable transitionsTime of day Occurence
03:00 09:00 12:00 15:00 21:00 percentage of time
number of days
Stable Well mixed Well mixed Well mixed Stable 6.0 40
Stable St Sc Sc Sc Stable St 2.4 16
Stable Stable Well mixed Stable Stable 1.2 8
Stable Well mixed Cu Cu Stable 1.2 8
Stable Well mixed Well mixed Well mixed Well mixed 1.2 8
12% of the time
“Textbook” boundary layer evolution
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Diurnal comparison:01/09/2009 – 31/08/2011
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Temporal comparison01/09/2009 – 31/08/2011
• Perfect match would have all numbers along diagonal.
• Stable/unstable distinction is well matched in model and observations
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Forecast skill
Symmetric extremal dependence index
(Ferro & Stephenson, 2011)
where and
ln ln ln(1 ) ln(1 )
ln ln ln(1 ) ln(1 )
F H H FSEDI
F H H F
aH
a c
b
Fb d
Event forecast
Event observed
Yes No
Yes a b
No c d
• A SEDI value of 1 indicates perfect forecasting skill.
• Robust for rare events
• Equitable• Difficult to hedge.
• Many different measures that could be used
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Forecast skill
random
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Forecast skill Stable?
random
a
d
b
c
• Model very skilful at predicting stability (day or night!)
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Forecast skill Cumulus present?
random
a
d
b
c• Not as skilful as stability but better than persistance
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Forecast skill Decoupled?
random
adb
c• Not significantly
better than persistence
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Forecast skill More than 1 cloudlayer?
random
adb
c
• Not significantly more skilful than a random forecast
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Forecast skill decoupled stratocuover a stable surface?
random
adb
c
• slightly more skilful than a persistence forecast
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Summary• Boundary layer processes are turbulent and are
parameterised in weather forecast models. • A new method using Doppler lidar and sonic
anemometer data diagnose observational boundary-layer type has been presented.
• Clear seasonal and diurnal cycle is present in the Met Office 4km model and observations with similar distributions.
• The model has the greatest skill at forecasting the correct stability, the other decisions are much less skilful.
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What next?
• Extend to other models without explicit types (e.g. ECMWF)
• Do same analysis over another site, possibly London
• Does misdiagnosis of the boundary-layer type affect the vertical distribution of pollutants and if so how long does this difference in pollutant distribution last?
• Can this be used to improve boundary-layer parameterisations?• Can observational mixing profiles be found using the
lidar ?